1. Technical Field
The present invention relates in general to improved grid computing and in particular to coordinating automated workload performance controllers within a grid computing environment. Still more particularly, the present invention relates to facilitating automated grid workload performance maintenance by multiple decisional grid modules that make decisions based on grid activity gathered from disparate types of grid resource groups.
2. Description of the Related Art
Ever since the first connection was made between two computer systems, new ways of transferring data, resources, and other information between two computer systems via a connection continue to develop. In typical network architectures, when two computer systems are exchanging data via a connection, one of the computer systems is considered a client sending requests and the other is considered a server processing the requests and returning results. In an effort to increase the speed at which requests are handled, server systems continue to expand in size and speed. Further, in an effort to handle peak periods when multiple requests are arriving every second, server systems are often joined together as a group and requests are distributed among the grouped servers. Multiple methods of grouping servers have developed such as clustering, multi-system shared data (sysplex) environments, and enterprise systems. With a cluster of servers, one server is typically designated to manage distribution of incoming requests and outgoing responses. The other servers typically operate in parallel to handle the distributed requests from clients. Thus, one of multiple servers in a cluster may service a client request without the client detecting that a cluster of servers is processing the request.
Typically, servers or groups of servers operate on a particular network platform, such as Unix or some variation of Unix, and provide a hosting environment for running applications. Each network platform may provide functions ranging from database integration, clustering services, and security to workload management and problem determination. Each network platform typically offers different implementations, semantic behaviors, and application programming interfaces (APIs).
Merely grouping servers together to expand processing power, however, is a limited method of improving efficiency of response times in a network. Thus, increasingly, within a company network, rather than just grouping servers, servers and groups of server systems are organized as distributed resources. There is an increased effort to collaborate, share data, share cycles, and improve other modes of interaction among servers within a company network and outside the company network. Further, there is an increased effort to outsource nonessential elements from one company network to that of a service provider network. Moreover, there is a movement to coordinate resource sharing between resources that are not subject to the same management system, but still address issues of security, policy, payment, and membership. For example, resources on an individual's desktop are not typically subject to the same management system as resources of a company server cluster. Even different administrative groups within a company network may implement distinct management systems.
The problems with decentralizing the resources available from servers and other computing systems operating on different network platforms, located in different regions, with different security protocols and each controlled by a different management system, has led to the development of Grid technologies using open standards for operating a grid environment. Grid environments support the sharing and coordinated use of diverse resources in dynamic, distributed, virtual organizations. A virtual organization is created within a grid environment when a selection of resources, from geographically distributed systems operated by different organizations with differing policies and management systems, is organized to handle a job request.
While clusters or other groups of servers can be grouped within a grid environment, Grid technologies do not solve all the problems to provide communication between groups of resources managed by different management systems with different standards. In particular, a current problem with Grid technology is the limitations of tools and systems that already monitor each group of systems. In particular, a limitation of standard performance monitors is that these monitors group resources according to the type of hardware resource. For example, a first monitoring tool may monitor pSeries machines while a second monitoring tool monitors systems grouped as zSeries machines (pSeries and zSeries are registered trademarks of International Business Machines Corporation). As a result of grouping resources according to hardware resource, these monitoring tools are limited to monitoring results of performance at a hardware levels. In addition, as a result of grouping resources according to hardware resource, these monitoring tools are limited to using the protocols implemented on the hardware resource and therefore typically not supporting communication directly between monitoring tools.
As clusters and other groups of systems are decentralized into grid environments, it would be advantageous to provide for tracking the grid based activity across disparate hardware and software platforms at an application level, rather than just a hardware level, and balancing grid jobs and grid workload across an entire grid environment including hardware, software, and network resources, rather than just a particular hardware environment. Thus, within a grid environment, it would be advantageous to monitor performance and other activity across the entire grid environment and coordinate distribution of that grid activity to modules affected by current grid activity.